The importance of integrating AI with existing business functions and tools

Arun ‘Rak’ Ramchandran, President & Global Head – Consulting & GenAI Practice, Hi-Tech & Professional Services, Hexaware, talks about their GenAI solutions, LLMs, data, cloud, privacy, and sustainability.

How does Hexaware strategically align Generative AI capabilities with the specific business goals of its clients?

Hexaware strategically aligns GenAI capabilities with specific business goals by employing a structured approach, including understanding client needs, addressing challenges like bias, compliance, and data security, and fostering collaboration. Our approach focuses on creating customized use cases, prioritizing those based on feasibility, relevance, and RoI, and integrating AI with existing business functions and tools.

Our customizable GenAI services and solutions power optimal business operations and create seamless AI journeys. Through our consulting framework, Decode AI, and execution framework, Encode AI, we cover the entire lifecycle to ensure rapid deployment and maximum value creation for diverse industry needs. We’re committed to disrupting ourselves, our clients, and industries by infusing GenAI into every aspect of service delivery.

How can organizations ensure data quality and traceability in GenAI solutions, and what methodologies maintain clear data lineage throughout the development lifecycle?

Ensuring data quality and traceability in GenAI solutions is paramount for reliability and effectiveness. At Hexaware, we advocate for a comprehensive approach to address these challenges.

Firstly, let’s talk about data quality. We advise organizations to establish a robust data governance framework, clearly defining roles and permissions regarding data access and actions. Additionally, implementing data validation checks during data ingestion and processing is crucial to maintain accuracy, consistency, and completeness. We also ensure regular data cleaning processes to address any missing, incorrect, or irrelevant data.

We advise organizations to establish a robust data governance framework, clearly defining roles and permissions regarding data access and actions.

Moving on to data traceability, it’s imperative to maintain clear data lineage records, documenting the origin and movement of data throughout its lifecycle. Version control systems should be utilized to track changes in data, models, and pipelines, facilitating understanding of the impact of these changes. While effective metadata management ensures comprehensive documentation of data sources, transformations, and usage.

Throughout the development lifecycle, continuous monitoring of data and models is essential to detect any anomalies or drifts in data distribution or model performance. Automation of data validation, data lineage tracking, and monitoring processes is highly recommended to minimize manual errors and enhance efficiency.

What strategies are crucial to safeguarding the integrity, privacy, and addressing ethical considerations associated with GenAI solutions?

At Hexaware, safeguarding the integrity, privacy, and ethical considerations of our GenAI solutions is our utmost priority. We’ve developed a comprehensive framework based on four key pillars: fairness, accountability, transparency, and reliability and security.

Fairness is foundational to our approach. We dive deep into our data to root out any biases to ensure that our solutions are delivering equitable outcomes for all. Accountability is essential in maintaining trust. We document our processes and decisions, including bias checks and third-party oversight, to make sure we’re answerable every step of the way.

Transparency is crucial for building confidence. We want our clients to know exactly what they’re getting with our GenAI solutions. That means being crystal clear about where our data comes from and how our models work.

And of course, we take privacy and security very seriously. We employ stringent measures to protect privacy, including anonymization of personal data, and regularly test and monitor our systems to maintain their integrity.

Innovation thrives with the ability to rapidly prototype and deploy AI models, while reliability is ensured through continuous monitoring.

Our assurance strategy empowers clients to evaluate and adopt GenAI responsibly, considering these dimensions. We address implications such as PII theft and regulatory compliance through solution groups encompassing standard operating procedures, organizational mandates, design mandates, and tools/assets, ensuring technologically advanced and ethically sound GenAI adoption.

We also stay updated on various responsible and ethical AI frameworks and regulations like those found in the US, EU, and China, as well as hyperscaler organizations like Microsoft. This allows us to advise, implement, and monitor them for our clients using suitable governance mechanisms.

In GenAI and hybrid cloud environments, what key features are essential for deploying and managing solutions, and what benefits do they offer to organizations?

When it comes to deploying and managing GenAI solutions in hybrid cloud environments, there are some must-have features that make all the difference. Scalability is key—think auto-scaling and distributed processing to handle those fluctuating workloads and massive datasets without breaking a sweat. And, of course, security and compliance features like data encryption and access control are non-negotiables to keep sensitive data safe and meet regulatory requirements.

Then there’s interoperability—having standard APIs and containerization tech ensure smooth deployment across different platforms. Monitoring and management tools, including performance tracking and automated updates, help keep AI models running smoothly.

Now, let’s talk about the benefits. Efficiency gets a boost as resources are used more effectively, cutting down on operational costs. Hybrid cloud setups offer flexibility, allowing you to tap into both on-premises and cloud resources as needed. Innovation thrives with the ability to rapidly prototype and deploy AI models, while reliability is ensured through continuous monitoring. Also, robust security features not only protect your data but also keep you compliant with regulations. Last but not least, cost efficiency kicks in as you streamline infrastructure maintenance and scaling expenses.

What challenges has Hexaware faced in scaling the development of GenAI solutions, and what strategies or technologies are being employed for efficient and reliable development?

Hexaware has encountered several challenges in scaling the development of GenAI solutions while ensuring efficiency, speed, and accuracy. One major challenge lies in ensuring data quality, as GenAI solutions heavily rely on accurate and unbiased input data. To address this, we prioritize meticulous data preparation, ensuring that data artifacts are accurate, current, and comprehensive.

Another challenge involves navigating legal and regulatory compliance, given the rapid evolution of GenAI. Engaging local legal and compliance experts is crucial to ensure that our solutions comply with regional and global regulations, especially as our user base expands.

It’s very important to maintain the authenticity and originality as there is a risk of produced content mirroring existing works. We work closely with clients’ legal teams to address potential legal implications, and solutions may incorporate features like content watermarking to meet client expectations.

Security and data privacy are paramount concerns, with organizations wary of potential data breaches. Our solutions include built-in guardrails to prevent data loss, ensuring confidential and personally identifiable information (PII) remains protected. Total Cost of Ownership (TCO) is also a consideration, particularly as clients transition from pilot phases to full-scale implementation. We provide clients with visibility into evolving costs, whether based on closed-source models (usage/API calls) or open-source models (hosting, compute, and maintenance).

Lastly, there’s a focus on sustainability impact, with clients expressing concerns about the environmental footprint of using LLMs. We guide clients in selecting models aligned with their ESG principles, ensuring that GenAI solutions are in line with organizational sustainability goals.

How does Hexaware leverage advancements in large language models (LLMs) to enhance GenAI capabilities, especially in natural language understanding and generation?

Hexaware leverages cutting-edge large language models (LLMs) to elevate our Generative AI capabilities, particularly in natural language understanding and generation. With access to advanced LLMs like GPT-4, Llama 2, and Mixtral 8x7B, we develop innovative solutions across various domains.

For instance, for enhanced customer interaction, LLMs power chatbots and virtual assistants to deliver more human-like experiences. Our Tensai platform seamlessly integrates static and dynamic data sources to enhance both agent and customer experiences.

It’s very important to maintain the authenticity and originality as there is a risk of produced content mirroring existing works.

Moreover, LLMs can facilitate content generation by producing human-like text for marketing, social media, and beyond. Our content hub solution acts as a central content generation engine, engaging with different LLMs to match specific requirements.

Additionally, LLMs support data analysis and augmentation by extracting insights from vast text datasets. Our product discovery tool automates product description generation for online listings, utilizing these capabilities. Also, LLMs contribute to training and education by creating personalized learning materials and offering intelligent tutoring. We offer a digital coaching solution that provides smart tutoring and evaluation powered by advanced LLMs.

LLMs also find applications in industry-specific use cases, such as our medical coding acceleration solution, which automates medical code deciphering. Advancements in LLMs present a significant opportunity for Hexaware to develop innovative solutions across various domains for our clients.

In the context of GenAI, how does the organization approach the selection and optimization of compute models to ensure efficient and scalable performance, considering the balance between efficiency and model accuracy?

In our approach to model selection and optimization within GenAI, we employ two primary methods, each with its nuances. Firstly, there’s Retrieval Augmented Generation (RAG), which allows us to efficiently retrieve relevant information by vectorizing our data. This method ensures scalability by minimizing the need for extensive adjustments to the chosen model while accommodating increasing data volumes. The organization often opts for this route as it requires minimal tinkering with the model architecture. The ‘generative’ aspect of RAG synthesizes summaries from the internal dataset, minimizing reliance on external knowledge.

Alternatively, we fine-tune models to enhance accuracy, leveraging our internal, that is, domain-specific, knowledge base. This fine-tuning process ensures that the models are tailored precisely to our requirements, striking a balance between efficiency and accuracy. We experiment with different scenarios, adjusting parameters and incorporating data augmentation techniques to refine the models further. By carefully navigating these approaches, we achieve scalable performance without compromising on accuracy or efficiency.

In the pursuit of unleashing the transformative power of generative AI to nurture ideas aligning with business goals, could you share a notable success story or case study where Hexaware’s Generative AI solutions significantly contributed to the achievement of specific business objectives for a client?

One instance that stands out is our collaboration with a prominent insurance client based in Europe. They needed to enhance the efficiency of their agents by leveraging data scattered across their repository of policy documentation and customer-specific information. Our Gen AI-led approach adhered to key principles: restricting responses to insurance-related topics and inquiries, concentrating on the client’s products and data while avoiding competitor comparisons, operating within the framework of a Belgium-based insurance company, and promptly deleting indexed data in the Vector Database post-session to maintain data privacy. This streamlined operations, empowering agents with actionable insights and driving tangible business outcomes. 

Arun ‘Rak’ Ramchandran

President & Global Head – Consulting & GenAI Practice,

Hi-Tech & Professional Services, Hexaware

By Minu Sirsalewala 

minus@cybermedia.co.in